Generating a model for positioning

ABSTRACT

A method for generating a model of an environment enabling positioning within the environment is disclosed. The method comprises: receiving distance-dependent measurements relating to a distance between a receiving unit position and a source position; receiving information relating to geographic positions, each defining a receiving unit position or a source position; forming a candidate model, which defines candidate source positions and candidate receiving unit positions; iteratively reducing a model error of said candidate model, wherein said model error comprises a weighted contribution from distance-dependent errors, which are based on a difference between a distance-dependent value between a candidate source position and a candidate receiving unit position and the corresponding distance-dependent measurement, and geographic position errors, which are based on a difference between a geographic position and a corresponding candidate position, and outputting, from said iterative reducing, determined locations for said plurality of sources to form said model of the environment.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/487,307, filed Apr. 13, 2017, which is a continuation of U.S. patentapplication Ser. No. 15/153,456, filed May 12, 2016, which claimspriority under 35 USC 119 to European Application No. 15167557.6, filedMay 13, 2015, in the European Patent Office (EPO), the disclosures ofwhich are incorporated by reference herein in their entireties.

BACKGROUND Field

The described technology relates, in general, to a system and method forcreating a model, which may be used for localizing devices in anenvironment, in particular, an indoor environment.

Description of the Related Art

A positioning system enables a mobile device to determine its position,and makes the position of the device available for position-basedservices such as navigating, tracking or monitoring, etc. GlobalNavigation Satellite System (GNSS), such as the Global PositioningSystem (GPS), is the most widely used positioning system and used invarious devices such as smart phones, tracking equipment, etc. Whileproviding very good and accurate outdoor location, there are somedrawbacks with GPS. For small and low cost battery operated electronicdevices, GPS is too expensive and draws too much power. GPS also cannotbe deployed for indoor use, because the required close to line-of-sighttransmission between receivers and satellites is not possible in anindoor environment.

SUMMARY OF CERTAIN INVENTIVE ASPECTS

It is an object of the described technology to enable a simple androbust method and system for generating a model of an environmentenabling positioning within the environment. It is a further object ofthe described technology to generate a model which allows accuratedetermination of locations of receiving units in the environment.

According to a first aspect, there is provided a method for generating amodel of an environment enabling positioning within the environment,said method comprising: receiving a plurality of distance-dependentmeasurements from one or more receiving units, each of saiddistance-dependent measurements providing information relating to adistance between a receiving unit position of the one or more receivingunits and a source position and said plurality of distance-dependentmeasurements providing information relating to distances betweenreceiving unit positions and source positions for a plurality ofsources; receiving information relating to a plurality of geographicpositions, each of said geographic positions defining a receiving unitposition or a source position; forming a candidate model, which definescandidate source positions for said plurality of source positions andcandidate receiving unit positions for said plurality of receiving unitpositions; iteratively reducing a model error of said candidate model,wherein said model error comprises a weighted contribution fromdistance-dependent errors, which are based on a difference between adistance-dependent value between a candidate source position and acandidate receiving unit position and the distance-dependent measurementrelated to the distance between the receiving unit position and thesource position, and geographic position errors, which are based on adifference between a geographic position and a candidate position forthe receiving unit position or source position, and outputting, fromsaid iterative reducing, determined locations for said plurality ofsources to form said model of the environment.

According to a second aspect, there is provided a system for generatinga model of an environment enabling positioning within the environment,said system comprising: a processing unit, said processing unit beingarranged to receive a plurality of distance-dependent measurements fromone or more receiving units, each of said distance-dependentmeasurements providing information relating to a distance between areceiving unit position of the one or more receiving units and a sourceposition and said plurality of distance-dependent measurements providinginformation relating to distances between receiving unit positions andsource positions for a plurality of sources, said processing unit beingfurther arranged to receive information relating to a plurality ofgeographic positions, each of said geographic positions defining areceiving unit position or a source position; wherein said processingunit comprises a model calculating unit, which is arranged to receivesaid plurality of distance-dependent measurements and said informationrelating to a plurality of geographic positions as input, wherein themodel calculating unit is configured to: form a candidate model, whichdefines candidate source positions for said plurality of sourcepositions and candidate receiving unit positions for said plurality ofreceiving unit positions; iteratively reduce a model error of saidcandidate model, wherein said model error comprises a weightedcontribution from distance-dependent errors, which are based on adifference between a distance-dependent value between a candidate sourceposition and a candidate receiving unit position and thedistance-dependent measurement related to the distance between thereceiving unit position and the source position, and geographic positionerrors, which are based on a difference between a geographic positionand a candidate position for the receiving unit position or sourceposition, and output, from said iterative reducing, determined locationsfor said plurality of sources to form said model of the environment.

According to the described technology, a method and system is provided,wherein a model of an environment can be generated without requiringpre-deployment efforts, such as building detailed RF maps forfingerprinting or adding building maps. Rather, by means of gathering aplurality of distance-dependent measurements, sufficient amounts ofinformation may be gathered allowing a model of the environment to becomputed. Thus, the model may be generated based on measurements thatmay be received from receiving units, such as mobile phones of users,over a period of time. The model may therefore be generated bycollecting measurements that are anyway made by the mobile phones andthere is no requirement on efforts by users or a deployer of the system.

Further, according to the described technology, a self-improving modelis facilitated. The model may be continuously or periodicallyre-generated based on further distance-dependent measurements that maybe received, e.g., when positioning devices after the model has beenlaunched.

Since information relating to a plurality of geographic positions isreceived, the model may provide localization globally by means of thegeographic positions relating relative positions determined by thedistance-dependent measurements to a global positioning system. Aplurality of geographic positions may also contribute to a scale to themodel. It should be realized that it is sufficient to receiveinformation relating to geographic positions defining a few of thereceiving unit positions or source positions and it is certainly notnecessary to receive information relating to geographic positions or allor even a majority of the receiving unit positions or source positions.

Further, the generated model may be used also for positioning of simplewireless devices. It may be advantageous to use advanced mobile devices,such as mobile phones, to gather the necessary information forgenerating the model. However, once the model has been generated, alocation may be quickly determined based on the model, and thelocalization may even be performed by a simple wireless device. Thus thesystem can use existing infrastructure in buildings, such as Wi-Fi APsor Bluetooth beacons, and be able to position low cost wireless devices,such as Wi-Fi receivers or Bluetooth receivers, with no extra hardwareadded.

Although the described technology is mainly described for indoorenvironments, it is to be understood that the method and system alsoworks outdoor. It may for instance be of use for devices that cannot useGPS or similar techniques.

It is further an insight of the described technology that labeling anyinformation as completely correct makes the model sensitive to errors insuch presumably correct information. Rather, according to the describedtechnology, all information received as input for generating the modelis treated as at least partly uncertain and such uncertainty isaccounted for when generating the model. In particular, informationrelating to geographic positions may be received in several differentmanners, which may generally be quite reliable. However, according tothe described technology, the model is generated by viewing thegeographic positions as potentially incorrect and it is possible togenerate a model wherein the model does not place the geographicposition exactly according to the received information. This impliesthat the generated model is particularly robust.

As used herein, the term “distance-dependent measurement” should beconstrued as a measurement that gives some indication of a distance,even though it may not measure the distance directly. Rather, themeasured quantity may be a quantity that is dependent on distance insome way. The measured quantity need not even be directly proportionalto the distance and may even be non-linearly related to the distance. Asfurther described below, the distance measurement may e.g. be based on areceived power of a signal, i.e. a signal strength, or be based on atime of travel of a signal between a source and a receiving unit. Also,for the use of the distance-dependent measurement in determining ofdistance-dependent errors, a non-linear scale, such as a logarithmicscale, may be used. Thus, the distance-dependent measurement may e.g. bea logarithmic value of a distance-dependent quantity. This also impliesthat for small distances, an error in distance will have large effect.

The model of the environment may define the environment from the pointof view of the type of information that is gathered to generate themodel. Thus, where RF signals are used for distance-dependentmeasurements, the model of the environment represents RF signals thatare emitted by the plurality of sources.

The sources may be any type of device that transmits a signal that maybe received by a receiving unit. It may be particularly advantageous touse sources that are already in extensive used and placed in theenvironment to be modeled. In this regard, sources may therefore beWi-Fi access points or Bluetooth beacons that are already frequentlyused. Of course, several different types of sources may besimultaneously used as input to the model. Also, base stations, such asLong-Term Evolution (LTE) pico base stations may constitute sources.

According to an embodiment, the plurality of sources comprise a sourcewhich is arranged to continuously transmit a radio-frequency signalallowing receiving units to discover the source. Thus, the source alwaysprovides a signal which may be picked up the receiving units.

The receiving unit may any type of device that is able to receive asignal transmitted by the source. It could be a mobile phone, acomputer, such as a laptop, or other processing devices. However, it mayalso be a simple device having a receiver. With the dawning of Internetof Things, it may be contemplated that such a receiver may be installedin any type of device, such as TVs, lighting devices, surveillancesystems, or domestic appliances.

It should be realized that the information relating to a geographicposition may be received in many different ways. For instance, it may bea measurement of a GPS position for the receiving unit or the source.However, the geographic position may alternatively be received from adatabase providing information of positions of sources, e.g. obtainedwhen installing the sources. As another alternative, the informationrelating to geographic positions may be received by means of a userindicating the positions of sources and/or receiving units on a map ofthe environment. The information relating to a geographic position maybe provided as location coordinates providing a longitude and latitudevalue, but the information may alternatively be provided in relation toa map or another coordinate system.

According to an embodiment, a receiving unit is arranged to determine ageographic position, and receiving information relating to a pluralityof geographic position measurements may comprise receiving a geographicposition measurement from the receiving unit defining a geographicposition for the receiving unit position. Receiving units, such asmobile phones, may have the capacity to determine geographic positions,e.g. through a GPS signal. Thus, such receiving units may be wellequipped to provide information relating to geographic positions in theform of geographic position measurements.

It should also be realized that the weights given to the contribution ofdistance-dependent errors and geographic position errors may differ notonly between the different types of errors, but also for differenterrors of the same type. The weights may be based on how reliable theinformation is that is weighted. For instance, a geographic position mayoften be more reliable than the distance-dependent measurements andtherefore geographic position errors may generally be given a higherweight. Further, information regarding the accuracy of adistance-dependent measurement or a geographic position may be gatheredand such accuracy information may then be used for providing anappropriate weight to the related error. In particular, it may bepossible to receive information regarding accuracy of GPS measurements.

The iterative reducing of a model error may be performed until anoptimization of the model error is received. However, it may not benecessary to perform optimization until a global or local minimum of themodel error is reached. In an alternative, the iterative reducing mayfor instance be performed until a model error is reached which is belowa desired threshold.

The iterative reducing may be started from qualified guesses ofcandidate source positions and candidate receiving unit positions. Bymeans of making a good guess, the iterative reducing may quickly reachan optimization of the model. Also, minimization of the model error to alocal minimum instead of to a global minimum may be avoided. It shouldalso be realized that several different set-ups of start values may beused and tested in order to find the best model.

The method for generating a model may require extensive computing powerin order to be able to handle huge volumes of data and to compute amodel based on these volumes of data. Hence, the method may be performedby a server, which is arranged to receive information from receivingunits. However, once a model has been generated, extensive computingpower is not necessary for determining locations based e.g. on measuredsignal strengths within the environment. Thus, the model may bedownloaded to receiving units and may thereafter be used therein for thedetermining of locations of the receiving units.

According to an embodiment, the distance-dependent measurements comprisepower-based distance measurements which represent a distance as ameasured power of a signal received by the receiving unit and emitted bythe source. Using power-based distance measurements, such as RSSmeasurements, allows the method for generating a model to use availablereceiving units, such as mobile phones, which may provide suchmeasurements without any need for setting up the receiving unit.

The measured power of the signal is dependent on a damping factor in theenvironment. In an embodiment, the candidate model further comprises atleast one candidate damping factor and said iterative reducing comprisesvarying the at least one damping factor in order to reduce the modelerror. This implies that the method may also take into account thedamping factor in the environment in order to generate the model. Thus,more information may need to be gathered in order to provide a reliableresult from the model. However, the generated model may also be evenmore accurately representing the environment.

The measured power of the signal is also dependent on an emitted powerof the source. In an embodiment, the candidate model further comprisesat least one candidate emitted power for said plurality of sources, andsaid iterative reducing comprises varying the at least one candidateemitted power in order to reduce the model error. Often sources may bemanufactured to emit signals with a specific power and if all sources inthe environment emit using the same power, using candidate emittedpowers may not substantially change the resulting model. However, ifsome sources emit weaker or stronger signals, providing candidateemitted powers may generate a more accurate model of the environment.

According to an embodiment, the method generates a two-dimensional (2D)model of the environment. This implies that two-dimensional coordinatesof locations may be provided, but a height of a position may not beprovided by the model. Such a model may provide positioning for aspecific floor of a building or may be suitable for one-floor buildings.In another embodiment, the method generates a partly limitedthree-dimensional model of the environment. For instance, the generatedmodel may allow determination of which floor of a building that aposition relates to, which implies that only discrete values of a heightdirection are allowed. This may be called a 2.5-dimensional model. Inyet another embodiment, the method generates a three-dimensional (3D)model of the environment.

According to an embodiment, the distance-dependent measurements comprisetime-based distance measurements which represent a distance as ameasured round-trip time of a signal exchanged between the receivingunit and the source. Time-based distance measurements may be available,at least within a near future, in standard mobile phones. It should berealized that time-based distance measurements may be solely used or maybe used in combination with power-based distance measurements. Thetime-based distance measurements may be more reliable than power-baseddistance measurements and corresponding weights may be given in theiterative reducing.

According to an embodiment, the iterative reducing uses only time-baseddistance measurements and the method further comprises associatinginformation representing properties of emitted signals in theenvironment with the determined locations for said plurality of sourcesin said model. Having determined locations for the plurality of sourcesbased on time-based distance measurements, the power-based measurementsmay be added to the model for determining e.g. properties of thebuilding. The power-based measurements may thus determine positions ofdoors, walls, or windows in the building.

According to an embodiment, the iterative reducing comprises calculatingderivatives for the distance-dependent errors and the geographicposition errors and using the derivatives to update the candidate modelin order to reduce the model error. During generation of the model, adirection for changing the model may be determined such that the modelerror may be reduced. By means of calculating derivatives for theerrors, such direction for changing the model may be obtained.

According to an embodiment, the distance-dependent measurements comprisesequential distance-dependent measurements for sequential receiving unitpositions, said sequential distance-dependent measurements beingrecorded in sequence during movement of a receiving unit, and whereinsaid model error comprises a weighted contribution from sequence errors,which are based on a difference between relations of candidate positionsfor the sequential receiving unit positions to each other and apre-determined expectation of relations of the sequential receiving unitpositions. A plurality of distance-dependent measurements may easily beobtained while a person is moving through the environment carrying thereceiving unit. By knowing that a number of distance-dependentmeasurements are sequential distance-dependent measurements, sequenceerrors may be set up in the generation of the model for improving theresulting model. For instance, it may be assumed that the person movesat a constant pace through the environment, which means that a sequenceerror may be provided if the candidate positions are not at equaldistance from each other.

According to an embodiment, the method further comprises receivingfurther distance-dependent measurements from one or more receivingunits, each of said further distance-dependent measurements providinginformation relating to distances between a receiving unit position ofthe one or more receiving units and source positions of said pluralityof sources, and improving said model of the environment based on thefurther distance-dependent measurements. The further distance-dependentmeasurements may easily be added to the information that has been usedfor generating the model in order to improve the model. This impliesthat the model may be continuously improved while it is being used. Suchcontinuous improvement is not possible when using positioning techniquesthat need an offline training phase.

According to a third aspect, there is provided a computer programproduct comprising a computer-readable medium with computer-readableinstructions such that when executed on a processing unit the computerprogram product will cause the processing unit to perform the methodaccording to the first aspect. Such a computer program product may thusprovide a possibility to install and execute the program in order toobtain the above-discussed advantages of the method.

According to a fourth aspect, there is provided a method for determininga location of a receiving unit, said method comprising: measuringdistance-dependent values of the receiving unit in relation to aplurality of sources, retrieving locations of said plurality of sources,wherein said locations have been determined by the method according tothe first aspect, and calculating a location of the receiving unit basedon the measured distance-dependent values and the retrieved locations ofsaid plurality of sources.

Thus, a method is provided for determining locations of devices, whichmethod makes use of the model that is generated as discussed above.According to an embodiment, the calculation of the location may beperformed using a trilateration algorithm. According to anotherembodiment, the calculation may be performed using a fingerprintalgorithm based on the model. According to yet another embodiment, thecalculation may be performed using a combination of such algorithms.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the described technology will now bedescribed in further detail, with reference to the appended drawingsshowing embodiment(s).

FIG. 1 is a schematic view of an indoor environment comprising sourcesand receiving unit positions.

FIG. 2 is a schematic view similar to FIG. 1, wherein the receiving unitpositions are indicated as a track along which a mobile device has beenmoved through the environment.

FIG. 3 is a schematic view illustrating relationship between sources andreceiving units.

FIG. 4 is a flow chart illustrating a method for generating a model ofan environment according to an embodiment.

FIG. 5 is a schematic view of a system for generating a model of anenvironment according to an embodiment.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE EMBODIMENTS

There are a number of alternative technologies that may be used forpositioning in indoor positioning systems (IPS) such as infrared (IR)radiation, ultrasound, radio-frequency identification (RFID), wirelesslocal area network (WLAN), e.g. Wi-Fi, Bluetooth, sensor networks,ultra-wideband (UWB), magnetic signals, vision analysis and audiblesound. Each developed system takes advantage of a particular positioningtechnology or combining some of these technologies, which also inheritsthe limitations of these technologies. The designers make tradeoffbetween the overall performance and the complexity of the IPSs.

Another indoor location technique is based on sensors, like compass,accelerometer and gyrator, and using these for pedestrian dead reckoning(PDR) where the relative position from an absolute position based on GPSor other is estimated. The main problem with these systems is drift dueto sensor inaccuracies. To overcome drift, these systems are used inhybrid positioning setup, where it is combined with some radio frequency(RF) positioning technique, such as Wi-Fi positioning. Using thesesystem also requires devices with many sensors and computing power, soit can mainly be used only by smart phones with special software (app orembedded) and consumes a lot of power. It is not practical to usecontinuously or in small inexpensive devices.

One localization technique used for positioning with wireless accesspoints (APs) is called fingerprinting and is based on an offlinelearning training phase of measuring the intensity of the receivedsignal strength (RSS) in different defined positions and record the“fingerprint” of RSS signals at that position in a database. When datais gathered in all positions, a device can look-up its position byrecording the RSS “fingerprint” and compare to closest fingerprint inthe database. While this method can give a good location performance,the extra effort is extensive in the pre-deployment phase to builddetailed RF maps and survey the building. Also since moving a singlewireless access point requires a new survey and a new RF map, thisapproach make it not practical more than for a few commercial buildingslike shopping malls, airports etc.

Another Wi-Fi localization approach is to use the RF indoor propagationmodel. A received signal strength from a transmitter may be modeledusing a log-distance path loss model, which in a simplified form can beexpressed as:Prx(dBm)=Ptx(dBm@1 m)−10*γ*log 10(d)−Pf(n)−N(s)

where

Prx(dBm) is the received signal power,

Ptx(dBm@1 m) is the transmit power measured by the receiver at 1 mdistance from the transmitter,

γ is the average RF damping factor, normally 2 for free space andbetween 1.5 and 6 for an indoor environment,

d is the distance between transmitter and receiver in meters

Pf(n) is an optional floor penetration loss factor

n is number of floors between transmitter and receiver, and

N is the random Gaussian noise with 0 mean and a standard deviation s.

This localization approach requires that the location of all APs areknown. Preferably the exact location of the APs is determined by somemanual survey of the building. Then if an electronic device measures theRSS signals of the APs, the position can be calculated by using the APs'location, indoor propagation models and trilateration algorithms. Thistechnique requires knowledge of the APs' location and the propagationconstants of the building like RF damping factor and floor penetrationloss factor. Even knowing these factors, it is challenging to get anaccurate location due to walls and high noise in the RSS signals due toreflections.

An alternative method to overcome these problems is to use time offlight measurements of signals between the transmitter and the receiver,also called round trip time (RTT) and fine timing measurements as beingcurrently specified in new 802.11mc standard. Using time measurements ofthe time of flight for a transmission between transmitter and receiverallows a much more accurate distance estimate between an AP and anelectronic device to be calculated independent of walls. Then if knowingthese distance estimates for an indoor positioning system, it is atrilateration calculation assuming all APs' locations are known.

Looking at most of the current indoor positioning techniques based on RF(Wi-Fi, Bluetooth, etc.), they require a good knowledge of the locationof the transmitters and some model of the RF environment in the building(fingerprints or propagation model). For improvement of positioningtechniques, in particular for indoor environments, it would therefore beof interest to provide an indoor location system that does not needpre-deployment or surveying and which may continuously improve theaccuracy of the APs' location and the RF model of the environment.

In U.S. Pat. No. 8,077,090, a simultaneous localization and RF modelingtechnique is disclosed, Mobile devices record RSS measurementscorresponding to APs at various unknown locations and report these RSSmeasurements as well as any other location fix to a localization server.A RF modeling algorithm runs on the server and is used to estimate thelocation of the APs using the recorded RSS measurements and any otheravailable location information. The method of U.S. Pat. No. 8,077,090thus improves on techniques requiring pre-deployment or surveying of theenvironment. In certain embodiments, it would be desirable to provide amore robust method of creating an RF model.

In cooperation with attached drawings, the technical contents anddetailed description of the described technology are describedthereinafter according to a preferable embodiment, being not used tolimit the claimed scope. The described technology may be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided forthoroughness and completeness, and fully convey the scope of thedescribed technology to the skilled person.

Reference will now be made to the drawings to describe the describedtechnology in detail.

In a first embodiment shown in FIG. 1, there is a single plane, 2Denvironment 101 with sources in the form of RF transmitters 102-104. Theenvironment 101 is here assumed to be mostly indoor, but could beoutdoor or indoor or a combination of the two. There are one or morereceiving units in the form of electronic devices that measure receivedsignal strength (RSS) from each of the transmitters 102-104 in at leastsame number of measurement positions 105 as the number of transmitters.

A plurality of geographic positions 106 is also provided. If twogeographic positions 106 are provided, a scale of the environment inrelation to an outer reference may be determined. Preferably, at least 3of the measurement positions 105 have a reference location obtained byGPS or some other mean. For a good location result, at least 2 of thesehas to be accurate, the third could be of low accuracy as obtained forexample by a GPS indoors below a roof window.

Assuming the measurement positions 105 and transmitters 102-104 arestrongly coupled, i.e. each measurement position 105 holds informationof RSS for each transmitter 102-104, as shown in FIG. 3, and thegeographic position for at least 3 measurement positions 105 are known,it is possible to uniquely solve the location of all transmitters102-104 and all measurement positions 105. Since the RSS measurementscontain a lot of noise, a bundle optimization is used to solve the mostlikely position of all transmitters 102-104 and receivers/measurementpositions 105. This bundle optimization keeps the reference location 106of the measurement positions within its accuracy and optimizes thelocation of all others so the error or residual is minimized. From theoptimization the standard deviation (accuracy) of each transmitter102-104 and each measurement position 105 can be calculated.

The bundle optimization may start with initial candidate positions forthe measurement positions and the transmitters. Such initial candidatepositions may be generated in a number of different ways, e.g. byrandomly guessing or by making some kind of qualified guess.

Further, the candidate positions may jointly form a candidate model,which may be optimized. An embodiment of the candidate model may be setup based on the following assumptions.

An estimation of positions of receiving units are provided by measuresof the received signal strength at a number of unknown measurementspositions m_(i)=(x_(i),y_(i)), where i is used to denote the index ofthe measurement position 105. The transmitters 102-104 are also inunknown positions w_(j)=(u_(j),v_(j)), where j is used to denote theindex of the transmitter. Received signal strength measurements P_(ij)are obtained from measurement position with index i to transmitter withindex j. Such measurements are obtained for a subset of all possibleindex pairs (i,j). This subset is denoted I and consists of all pairs(i,j) for which there exist a measurement from measurement position i totransmitter j. Let f(m,w) be a relevant model for power measurement. Inone embodiment, a stochastic measurement model is used, where P_(ij) isa stochastic variable with Gaussian distribution with mean f(m,w) andstandard deviation σ. One possible model is to usef(m,w)=A−10*γ*log 10(d(m,w)),withd(m,w)=√{square root over ((m−w)^(T)(m−w))}

and with values of σ being 4 dB.

The unknown parameters may be collected in a column vector, e.g. z=(w₁,. . . , w_(n1), m₁, . . . , m_(n2)). Assuming that all power measurementerrors are independent, the maximum likelihood estimate of theparameters is found by maximizing the product of the likelihood. This isequivalent to minimizing the sum of the negative logarithm of thelikelihood. Assuming noise with Gaussian distribution this becomes

$\sum\limits_{({i,j})}^{I}\;\left( {P_{i,j} - {f\left( {m_{i},w_{j}} \right)}^{2}} \right.$

This can be rephrased as

$\sum\limits_{r}\; k^{2}$

where r is a column vector containing all the residuals of type(P_(ij)−f(m_(i),w_(i))).

Thus, the estimation can be written as minimizing |r(z)|² over theunknown parameters z. It should be realized that a logarithmic, or othernon-linear scale of the estimated distance values, may be used, suchthat the minimizing of |r(z)|² for e.g. RSS power measurements in dBm,is equivalent to minimizing logarithmic values of the model andestimated distances |log 10(d_(ij))−log 10(d(m,w))|².

The bundle optimization may be arranged to perform iterations, wherein amodel error, as described above, of a candidate model is reduced. Themodel error comprises distance-dependent errors, which are based on adifference between a representation of a distance between a candidatesource position and a candidate measurement position according to thecandidate model, e.g. as a power-based representation in a logarithmicscale, and the corresponding distance as measured between thetransmitter 102-104 and the measurement position 105. The model errorfurther comprises geographic position errors, which are based on adifference between a candidate position and the corresponding referencelocation 106 as determined. Further errors may also be included based onother types of information being gathered from the environment, as willbe further described below.

The model error is based on weighted combinations, for instance aweighted sum, of the contributing errors. The weights may be chosen inaccordance with the accuracy or reliability of the correspondingmeasurements of the environment. Each model error may be weighted inaccordance with a standard deviation of the input for the error. Forinstance, the error may be divided by the standard deviation. Forinstance, a GPS measurement of a geographic position may have a standarddeviation of 10-20 m, whereas a manual positioning on a map may have astandard deviation of 1 m. Further, the geographic errors may also begiven a generally higher weight than the distance-dependent errors, suchthat the geographic errors may be multiplied with a constant, e.g. 10.

The bundle optimization may be run in order to find a global minimum ofthe model error. The optimization problem is however difficult to solve,because there are many local optima. According to one embodiment,several different strategies for finding a good starting point maytherefore be tried and then non-linear optimization techniques may beused for finding the closest local minima.

There are several possible strategies for finding a good initialestimate. Here we present three possible strategies.

-   -   1. Initialize z randomly    -   2. Initialize such measurement positions for which there is an        alternative localization method, e.g. using GPS. Then use        trilateration methods to obtain initial estimates of the        remaining parameters if possible. For those that cannot be        calculated, initialize them randomly.    -   3. Use minimal solvers and random sampling consensus to obtain        initial estimates of the parameters.

The random sampling consensus paradigm is based on randomly selecting aminimal subset of the data, that still allows for estimation of theparameters. For each such subset one solves for the unknown parameters.There are several solutions to this sub-problem. Nevertheless eachsolution associated with each subset can then be verified with remainingdata. This makes it possible to both remove outliers and also to obtainhigh-quality initial estimates to the parameters, to be used insubsequent non-linear optimization.

For the case of measurement positions and transmitters in twodimensions, the minimal configuration is 3 measurement positions and 3transmitters. These give rise to 9 power measurements. For each set of 9power measurements there are 8 possible solutions. Algorithms forcalculating all solutions from the power measurements in an efficientand numerically stable manner may be created as described above.

In another embodiment, the transmitters 102-104 in above description canbe a combination of different types, for example a combination of Wi-FiAPs and Bluetooth low energy (BLE) beacons.

In another embodiment, all or some of the measurements are made as atrack, see FIG. 2. Thus the same electronic device has made multiplemeasurements at regular intervals that are not too far apart to beuncorrelated. The bundle optimization then also includes the assumptionthat the speed and/or direction is constant between points, and adds aresidual for any deviation from this assumption. There are a number ofways to implement this residual, for example using the time stampbetween three points and assume that the middle point should always beon the line between the outer points. The weighting of this residualcompared to the other residual is important. A too high weight on thetrack means that the measurement positions may be forced to a straightline or equidistantly spaced positions, a too low weight will mean thatthe track will not impact the measurement positions 105.

In another embodiment, the model is optimized using an average RFdamping factor for the indoor environment. It is a well-known problemthat different buildings have very different RF propagation properties.A building with only thin walls or glass walls have a low damping factorand a building with many thick concrete walls have a high dampingfactor. Assuming the building structure is somewhat uniform, an averageRF damping factor gives a much better result for the optimizations thanusing a fixed RF damping factor given by International TelecommunicationUnion (ITU) models or similar estimate.

In yet another embodiment, the model is optimized using a RF dampingfactor individually for each transmitter 102-104. Thus, for transmitters102-104 located in corridors, the damping factor within the corridor isclose to 2 or even below 2. For a transmitter 102-104 behind many walls,the damping factor could be as high as 5-6. This means that for eachtransmitter 102-104, the model is optimized not just for location x andy for lowest residual, but also damping factor γ. It is understood thatmore measurement data is now needed for the optimization since there are3 unknowns for each transmitter 102-104: x, y and γ.

Similarly, in another embodiment, the model may be optimized for thetransmitter output power at one meter for each transmitter 102-104. Insome embodiments, this value should not differ very much between Wi-FiAPs since e.g. Wi-Fi APs for use in the 2.4 GHz band normally havespecified output power (100 mW). However, there are APs that allowtransmit power regulation and the transmitter 102-104 can be placedunfavorably, for example behind a wall. Also, Wi-Fi APs for use in the 5GHz band, may often have differing output powers, e.g. varying between50 mW and 1 W. Even more measurement data is now needed since there are4 unknown parameters optimized for each transmitter 102-104 x, y, γ andPtx(@1 m).

In another embodiment, timing measurements of RF signals, often calledRTT or fine timing measurement, are used instead of RSS measurements.The RF signals travels at the speed of light (3*10{circumflex over ( )}8m/s) and tests with Wi-Fi APs according to new 802.11mc specificationshows a measurement accuracy down to 1 ns. This means a theoreticalspatial resolution of 0.3 m. However, reflections and 2.4 GHz resonancewith water make resolution around 1 m. Still, RTT measurements resultsin a much more accurate estimate of distance between transmitter 102-104and receiver and low dependency of walls and floors obstruction.

In another embodiment, a combination of RSS and RTT measurements is usedto estimate all transmitters 102-104 and measurement positions 105.Since probably not all APs and devices will support RTT in the future,this is a likely situation. RTT measurements are used in theoptimization with a higher weight than just RSS measurements. Since thestandard deviation is smaller for RTT, the location result of theoptimization will be based on RTT primarily and with RSS as supportinginformation.

In yet another embodiment, all the above mentioned embodiments for RSSare used for 3D modeling with the z-axis (height) values just beingallowed in discrete steps (floors). The optimization is made as aniterative 2D optimization for measurement positions and APs for eachfloor. A floor attenuation factor for APs seen by measurement positions105 on adjacent floors has to be added in the optimization. To get agood starting value of which measurement position 105 and which AP is onwhich floor, a cluster algorithm or any other initialization may beapplied. Also, other sensors, such as a pressure sensor, could give agood starting value, or a final value, for the floor of the measurementpositions. The iterative optimization of the model error could beconstrained to allow or not allow movement of measurement positionsbetween floors.

In another embodiment, a full 3D model is created by the measurements.Thus for each AP and each measurement position 105, the x, y and z areestimated. For a full 3D model optimization, the noise in RSSmeasurements may be too high, so RTT measurements are preferably used.Many RTT measurements or similar timing measurements are necessary,since a much higher accuracy is required to get a good enough model. Atleast 3 known measurement positions providing geographic positioninformation are required, of which 2 has to be on same floor.

If there are a lot of data (APs and/or measurement positions 105 for abuilding, the optimization calculation is quite expensive in terms ofcomputational power. In another embodiment, the results from theoptimizations are stored in a memory for later use. For a new locationrequest of a measurement position 105, the location calculation is madeby a simple trilateration algorithms and the stored position of the APs.This computation is very simple and can be made either on the locationserver or in the device itself assuming it knows the surrounding APs.

In another embodiment, the location calculation for a new locationrequest of a new measurement position is made by using fingerprintingalgorithms and the stored position of the measurement positions 105.

In yet another embodiment, the location calculation for a newmeasurement position is made by taking the average of the trilaterationlocation based on APs and the fingerprint location based on measurementpositions 105.

It is possible to improve a RSS-based model if there are RTTmeasurements available. In an embodiment, an RF model or heat map of RSSmeasurements is created using measurement positions 105 generated fromRTT measurements. In this case the RSS measurements are not useddirectly for generating the measurement positions 105 but mapped on theenvironment. Thus RTT measurements are being used for calculating theAPs location and RSS measurements are being used to model the RFproperties of the building better. Both average building damping factor,floor attenuation factor and each APs damping factor can be estimatedand used for better positioning.

In another embodiment using the improved RSS model it is even possibleto estimate the location of walls and their RF properties. For example,if the average received power from an AP at a certain measurementposition 105 is X and the average received power at another measurementposition 105 1 m further from the AP is X-10 dB, it can be derived thereis a wall between them with a wall attenuation factor close to 10 dB.

In another embodiment, the improved RSS model is used with RF heat mapsfrom each AP to do a positioning of a simple device that can only scanRSS from APs. The most likely position is calculated by usingtrilateration of likely positions from each AP.

In another embodiment, the improved RSS model is used for generatingfingerprints of each measurement position 105 and for positioning of asimple device by using fingerprinting algorithms for finding most likelyposition.

In yet another embodiment, RTT measurements are used together with trackdata to generate a map of possible paths in the building. Since it ispossible to calculate each location with enough good accuracy with RTT,all possible paths walking around in the building can be stored. Ifthere are enough paths stored, it is possible to overlay all these datato find and map building structures such as doors, halls, stairs,elevators, etc.

Referring now to FIG. 4, a method for generating a model of anenvironment will be described. The method may be performed in a serveror any type of powerful computing resource. The server receives aplurality of distance-dependent measurements, step 402, such as RSS orRTT, providing information relating to a distance between a measurementposition and a transmitter. The server also receives informationrelating to a plurality of geographic positions, step 404, which mayprovide a reference location for a measurement position or atransmitter.

The server may then form a candidate model, step 406, defining candidatesource positions for the transmitters and candidate receiving unitpositions for the measurement positions.

Then, a model error of the candidate model is iteratively reduced, step408, in order to find a best combination of positions of thetransmitters and the measurement positions. Finally, the determinedlocations for the transmitters may be output, 410 to form the model ofthe environment.

The model may be stored in the server to provide a possibility todetermine locations of devices as new measurements are received fromreceiving units in the environment. Alternatively, the model may bedownloaded to clients, e.g. the receiving units, in order to provide apossibility to perform localization locally in the device. The receivingunit may detect when a new environment is entered and may then downloadthe corresponding model in order to perform localization in the newenvironment.

Referring now to FIG. 5, a system 500 for generating a model of anenvironment will be described. The system 500 comprises a computerresource 502 having a processing unit 504 for performing computations inorder to generate the model. The processing unit 504 may be providedwith a computer program controlling the processing unit 504, such as acentral processing unit (CPU), a graphics processing unit (GPU), togenerate the model. In an alternative, an application-specificintegrated circuit (ASIC) or field-programmable gate array (FPGA) may bearranged to control the generation of the model.

The computer resource 502 further comprises a communication unit 506 forreceiving information from receiving units 508, which measuredistance-dependent values to transmitters. The communication unit 506may also be arranged to receive information relating to geographicpositions, such as reference locations for measurement positions of thereceiving units. The communication unit 506 may be arranged to transferthe received information to the processing unit 504 as input forgenerating the model.

When the processing unit 504 has generated the model, it may be storedin the computer resource 502 in order to allow determination oflocations based on the model by means of the computer resource 502. Thegenerated model may also, or alternatively, be downloaded to receivingunits 508 in order to allow determination of locations of the receivingunits 508 within the receiving units 508 themselves.

Information and signals disclosed herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the above description may berepresented by voltages, currents, electromagnetic waves, magneticfields or particles, optical fields or particles, or any combinationthereof.

The various illustrative logical blocks, and algorithm steps describedin connection with the embodiments disclosed herein may be implementedas electronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof. Such techniques may beimplemented in any of a variety of devices such as general purposescomputers, wireless communication device handsets, or integrated circuitdevices having multiple uses including applications in wirelesscommunication device handsets, automotive, appliances, wearables, and/orother devices. Any features described as devices or components may beimplemented together in an integrated logic device or separately asdiscrete but interoperable logic devices. If implemented in software,the techniques may be realized at least in part by a computer-readabledata storage medium comprising program code including instructions that,when executed, performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas random access memory (RAM) such as synchronous dynamic random accessmemory (SDRAM), read-only memory (ROM), non-volatile random accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates program code in the form of instructions or datastructures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software or hardware configured for encoding and decoding, orincorporated in a combined video encoder-decoder (CODEC). Also, thetechniques could be fully implemented in one or more circuits or logicelements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components, orunits are described in this disclosure to emphasize functional aspectsof devices configured to perform the disclosed techniques, but do notnecessarily require realization by different hardware units. Rather, asdescribed above, various units may be combined in a codec hardware unitor provided by a collection of inter-operative hardware units, includingone or more processors as described above, in conjunction with suitablesoftware and/or firmware.

Although the foregoing has been described in connection with variousdifferent embodiments, features or elements from one embodiment may becombined with other embodiments without departing from the teachings ofthis disclosure. However, the combinations of features between therespective embodiments are not necessarily limited thereto. Variousembodiments of the disclosure have been described. These and otherembodiments are within the scope of the following claims.

The person skilled in the art realizes that the inventive technology byno means is limited to the preferred embodiments described above. On thecontrary, many modifications and variations are possible within thescope of the appended claims.

What is claimed is:
 1. A method for determining a location of a device,said method comprising: receiving first power-based distancemeasurements, each measurement representing a distance from the deviceto a source as a measured power of a signal received by the device;accessing a model defining source locations and radio frequencypropagation properties in an environment, said source locations andradio frequency propagation properties being determined based on acollection of second power-based distance measurements representing adistance from receiving units to source locations and at least one of:time-based distance measurements representing a distance from receivingunits to source locations, sequential receiving unit positions, compassdata, accelerometer data, gyrator data, sensor data indicating arelative floor in a building, and data correlating measurements atregular intervals, wherein said second power-based distance measurementsare acquired prior to acquiring of the first power-based distancemeasurements and said receiving units are different from the device; anddetermining a location of the device based on the first power-baseddistance measurements and the source locations, which are based on thesecond power-based distance measurements, and the radio frequencypropagation properties defined by the model, wherein the modelrepresents an indoor environment of a multi-floor building, the methodfurther comprising determining a location of the device in themulti-floor building, the location including at least a floor indicationand a position on the floor, based on the received first power-baseddistance measurements and the model.
 2. The method according to claim 1,wherein the model represents a 2.5-dimensional environment.
 3. Themethod according to claim 1, wherein the floor sensor value is receivedfrom a pressure sensor.
 4. The method according to claim 1, furthercomprising the device downloading the model from a server.
 5. The methodaccording to claim 1, wherein the model defines radio frequencypropagation properties using an average building damping factor.
 6. Themethod according to claim 1, wherein the model defines radio frequencypropagation properties including location of walls and radio frequencypropagation properties of the walls.
 7. The method according to claim 1,wherein the determining of the location comprises calculating thelocation using trilateration of likely positions from each source. 8.The method according to claim 1, wherein the determining of the locationcomprises using fingerprinting algorithms based on the model for findinglikely position of the device.
 9. The method according to claim 1,wherein the device is a Wi-Fi receiver or Bluetooth receiver or acellular receiver and sources are Wi-Fi access points or Bluetoothbeacons or cellular base stations, for example Long-Term Evolution picobase stations.
 10. The method according to claim 1, wherein a sequenceof first power-based distance measurements is received, the determiningof the location of the device being based on bundle optimization of thesequence of first power-based distance measurements in relation to themodel.
 11. The method according to claim 10, wherein the bundleoptimization includes an assumption of speed and/or direction betweenpoints in the sequence.
 12. The method according to claim 10, whereinthe model is used as an initial estimate for the bundle optimization.13. The method according to claim 10, further comprising forming a trackof sequence of positions of the device, wherein the bundle optimizationis used for locating the device in the sequence of positions along thetrack based on assumptions of speed and/or direction between points inthe sequence as a residual to the bundle optimization in locating thedevice along the track.
 14. The method according to claim 13, whereindetermining the location of the device in the sequence of positionscomprises forming a model by a previous bundle optimization as aninitial estimate of the model for determining the location of thedevice, and forming another bundle optimization using the firstpower-based distance measurement positions in the sequence of positionsto update the initial estimate of the model and determine location ofthe device based on the first power-based distance measurementpositions.
 15. An apparatus for determining a location of a device; saidapparatus comprising: a storage unit configured to receive and storefirst power-based distance measurements, each measurement representing adistance from the device to a source as a measured power of a signalreceived by the device; and a processing unit configured to: access amodel defining source locations and radio frequency propagationproperties in an environment, said source locations and radio frequencypropagation properties being determined based on a collection of secondpower-based distance measurements representing a distance from receivingunits to source locations and at least one of: time-based distancemeasurements representing a distance from receiving units to sourcelocations, sequential receiving unit positions, compass data,accelerometer data, gyrator data, sensor data indicating a relativefloor in a building, and data correlating measurements at regularintervals, wherein said second power-based distance measurements areacquired prior to acquiring of the first power-based distancemeasurements and said receiving units are different from the device; anddetermine a location of the device based on the received power-baseddistance measurements and the source locations, which are based on thesecond power-based distance measurements, and the radio frequencypropagation properties defined by the model, wherein the modelrepresents an indoor environment of a multi-floor building, theprocessing unit further configured to determine a location of the devicein the multi-floor building, the location including at least a floorindication and a position on the floor, based on the received firstpower-based distance measurements and the model.
 16. The apparatusaccording to claim 15, wherein fingerprinting algorithms based on themodel for finding likely position of the device are used to determinethe location of the device.
 17. The apparatus according to claim 15,wherein the device is a Wi-Fi receiver or Bluetooth receiver or acellular receiver and sources are Wi-Fi access points or Bluetoothbeacons or cellular base stations, for example Long-Term Evolution picobase stations.
 18. The apparatus according to claim 15, wherein thefirst power-based distance measurements comprise a sequence of firstpower-based distance measurements, and wherein the processing unitdetermines the location of the device based on bundle optimization ofthe sequence of first power-based distance measurements in relation tothe model.